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Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model
Over the years, the frequency and intensity of wildfires have increased due to climate change, old fire management practices, and other environmental factors [1]. These wildfires pose a threat to ecosystems, infrastructure, property, and lives, particularly in highly susceptible Mediterranean region...
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| Format: | Thesis |
| Language: | English English |
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Department of Electrical Engineering
2025
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| _version_ | 1867613197297516544 |
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| access_status_str | Open Access |
| author | Makhaba, Limpho Mapulane |
| author2 | Winberg, Simon |
| author_browse | Makhaba, Limpho Mapulane Winberg, Simon |
| author_facet | Winberg, Simon Makhaba, Limpho Mapulane |
| author_sort | Makhaba, Limpho Mapulane |
| collection | Thesis |
| description | Over the years, the frequency and intensity of wildfires have increased due to climate change, old fire management practices, and other environmental factors [1]. These wildfires pose a threat to ecosystems, infrastructure, property, and lives, particularly in highly susceptible Mediterranean regions, with suitable climatic conditions characterized by hot, dry summers and cool, humid winters [2]. Although fire is essential to maintaining such ecosystems' health, it can destroy long-term environmental sustainability and cause devastating destruction [1, 3]. Wildfire spread prediction from the ignition point to the surrounding area is a complex phenomenon affected by multiple variables such as weather, topography, and land-cover characteristics. Traditional systems used in wildfire spread prediction are often limited in accounting for the dynamic and complex factors influencing fire behaviour. To address this challenge, this research uses a multidisciplinary approach that combines Machine Learning techniques, remote sensing data, and wildfire science to develop an advanced wildfire prediction system. This research performs a comprehensive investigation into the use of supervised classification Machine Learning(ML) models, specifically, Artificial Neural Networks(ANN), Convolutional Autoencoder, and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and Sentinel Hub burned scars satellite data to train a logistic method for simulating fire burn maps. The ML models were then trained, tested, and validated using this data. An analysis of the models' capabilities in predicting the propagation of wildfire spread, as well as the driving factors that affect wildfire behaviour, was performed. The following evaluation matrices: loss, precision, recall, Area Under the Curve-Precision Recall(AUC_PR) and F1 score are used to assess the performance of the models in predicting the spatial and temporal properties of wildfire spread. The results indicate that the convLSTM can predict both temporal and spatial properties of fire, while the Convolutional Autoencoder can predict only the spatial properties of fire with minimal loss. The ANN produced the least satisfying results in predicting fire's spatial properties. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/42379 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:32:18.917Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/42379 Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model Makhaba, Limpho Mapulane Winberg, Simon Machine Learning Remote Sensing Satellite Data Deep Learning Over the years, the frequency and intensity of wildfires have increased due to climate change, old fire management practices, and other environmental factors [1]. These wildfires pose a threat to ecosystems, infrastructure, property, and lives, particularly in highly susceptible Mediterranean regions, with suitable climatic conditions characterized by hot, dry summers and cool, humid winters [2]. Although fire is essential to maintaining such ecosystems' health, it can destroy long-term environmental sustainability and cause devastating destruction [1, 3]. Wildfire spread prediction from the ignition point to the surrounding area is a complex phenomenon affected by multiple variables such as weather, topography, and land-cover characteristics. Traditional systems used in wildfire spread prediction are often limited in accounting for the dynamic and complex factors influencing fire behaviour. To address this challenge, this research uses a multidisciplinary approach that combines Machine Learning techniques, remote sensing data, and wildfire science to develop an advanced wildfire prediction system. This research performs a comprehensive investigation into the use of supervised classification Machine Learning(ML) models, specifically, Artificial Neural Networks(ANN), Convolutional Autoencoder, and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and Sentinel Hub burned scars satellite data to train a logistic method for simulating fire burn maps. The ML models were then trained, tested, and validated using this data. An analysis of the models' capabilities in predicting the propagation of wildfire spread, as well as the driving factors that affect wildfire behaviour, was performed. The following evaluation matrices: loss, precision, recall, Area Under the Curve-Precision Recall(AUC_PR) and F1 score are used to assess the performance of the models in predicting the spatial and temporal properties of wildfire spread. The results indicate that the convLSTM can predict both temporal and spatial properties of fire, while the Convolutional Autoencoder can predict only the spatial properties of fire with minimal loss. The ANN produced the least satisfying results in predicting fire's spatial properties. 2025-12-02T13:09:18Z 2025-12-02T13:09:18Z 2025 2025-12-02T13:00:05Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/42379 en eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Machine Learning Remote Sensing Satellite Data Deep Learning Makhaba, Limpho Mapulane Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model |
| thesis_degree_str | Master's |
| title | Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model |
| title_full | Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model |
| title_fullStr | Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model |
| title_full_unstemmed | Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model |
| title_short | Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model |
| title_sort | wildfire path spread prediction system using machine learning use of ann convolutional autoencoder and convlstm ml model |
| topic | Machine Learning Remote Sensing Satellite Data Deep Learning |
| url | http://hdl.handle.net/11427/42379 |
| work_keys_str_mv | AT makhabalimphomapulane wildfirepathspreadpredictionsystemusingmachinelearninguseofannconvolutionalautoencoderandconvlstmmlmodel |